CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images

Authors: Connor Jerzak | Adel Daoud

Paper Code .bib

Abstract: The causalimages R package enables causal inference with image and image sequence data, providing new tools for integrating novel data sources like satellite and bio-medical imagery into the study of cause and effect. One set of functions enables image-based causal inference analyses. For example, one key function decomposes treatment effect heterogeneity by images using an interpretable Bayesian framework. This allows for determining which types of images or image sequences are most responsive to interventions. A second modeling function allows researchers to control for confounding using images. The package also allows investigators to produce embeddings that serve as vector summaries of the image or video content. Finally, infrastructural functions are also provided, such as tools for writing large-scale image and image sequence data as sequentialized byte strings for more rapid image analysis. causalimages therefore opens new capabilities for causal inference in R, letting researchers use informative imagery in substantive analyses in a fast and accessible manner.

Research visualization 1 Research visualization 1 (dark mode)

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References

Connor T. Jerzak, Adel Daoud. CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images. ArXiv Preprint, 2023.
@article{jerzak2023causalimages,
  title={CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images},
  author={Jerzak, Connor T. and Adel Daoud},
  journal={ArXiv Preprint},
  year={2023},
  volume={},
  number={},
  pages={},
  publisher={}
}

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Related Work

Adel Daoud, Cindy Conlin, Connor T. Jerzak. Chinese vs. World Bank Development Projects: Insights from Earth Observation and Computer Vision on Wealth Gains in Africa, 2002-2013. World Development, 202: 107328, 2026.
@article{daoud2026chinese,
  title={Chinese vs. World Bank Development Projects: Insights from Earth Observation and Computer Vision on Wealth Gains in Africa, 2002-2013},
  author={Daoud, Adel and Cindy Conlin and Connor T. Jerzak},
  journal={World Development},
  year={2026},
  volume={202},
  pages={107328},
  publisher={}
}
[Overview][Data]

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Markus B. Pettersson, Connor T. Jerzak, Adel Daoud. Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: 'One Map, Many Trials' in Satellite-Driven Poverty Analysis. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-2026), Special Track on AI for Social Impact, 40: 39106-39115, 2026.
@article{pettersson2026debiasing,
  title={Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: 'One Map, Many Trials' in Satellite-Driven Poverty Analysis},
  author={Pettersson, Markus B. and Connor T. Jerzak and Adel Daoud},
  journal={Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-2026), Special Track on AI for Social Impact},
  year={2026},
  volume={40},
  pages={39106-39115},
  publisher={}
}
[Overview][Code][>]

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Adel Daoud, Connor T. Jerzak. Planetary Causal Inference: Understanding the Environment, Society, and Economy through Earth Observation and AI Systems. A Book Project, 2026+.
@article{daoud2026+planetary,
  title={Planetary Causal Inference: Understanding the Environment, Society, and Economy through Earth Observation and AI Systems},
  author={Daoud, Adel and Connor T. Jerzak},
  journal={A Book Project},
  year={2026+},
  volume={},
  pages={},
  publisher={Under contract with Cambridge University Press}
}

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Warren Zhu Fucheng, Connor T. Jerzak, Adel Daoud. Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTs. Proceedings of the Fourth Conference on Causal Learning and Reasoning (CLeaR), Proceedings of Machine Learning Research (PMLR), 2025.
@article{fucheng2025optimizing,
  title={Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTs},
  author={Fucheng, Warren Zhu and Connor T. Jerzak and Adel Daoud},
  journal={Proceedings of the Fourth Conference on Causal Learning and Reasoning (CLeaR), Proceedings of Machine Learning Research (PMLR)},
  year={2025},
  volume={},
  pages={},
  publisher={}
}
[Overview][Data][Code]
[Video][>]

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Connor T. Jerzak, Ritwik Vashistha, Adel Daoud. Effect Heterogeneity with Earth Observation in Randomized Controlled Trials: Exploring the Role of Data, Model, and Evaluation Metric Choice. ArXiv Preprint, 2024.
@article{jerzak2024effect,
  title={Effect Heterogeneity with Earth Observation in Randomized Controlled Trials: Exploring the Role of Data, Model, and Evaluation Metric Choice},
  author={Jerzak, Connor T. and Ritwik Vashistha and Adel Daoud},
  journal={ArXiv Preprint},
  year={2024},
  volume={},
  pages={},
  publisher={}
}
[Overview]

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Connor T. Jerzak, Fredrik Johansson, Adel Daoud. Image-based Treatment Effect Heterogeneity. Proceedings of the Second Conference on Causal Learning and Reasoning (CLeaR), Proceedings of Machine Learning Research (PMLR), 213: 531-552, 2023.
@article{jerzak2023image,
  title={Image-based Treatment Effect Heterogeneity},
  author={Jerzak, Connor T. and Fredrik Johansson and Adel Daoud},
  journal={Proceedings of the Second Conference on Causal Learning and Reasoning (CLeaR), Proceedings of Machine Learning Research (PMLR)},
  year={2023},
  volume={213},
  pages={531-552},
  publisher={}
}
[Overview][Data][Code]

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Kazuki Sakamoto, Connor T. Jerzak, Adel Daoud. A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty. To appear in: Hall, Ola and Ibrahim Wahab (eds.), Geography of Poverty, 2024.
@article{sakamoto2024scoping,
  title={A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty},
  author={Sakamoto, Kazuki and Connor T. Jerzak and Adel Daoud},
  journal={To appear in: Hall, Ola and Ibrahim Wahab (eds.), Geography of Poverty},
  year={2024},
  volume={},
  pages={},
  publisher={Edward Elgar Publishing (Cheltenham, UK)}
}
[Overview][Data]

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Connor T. Jerzak, Fredrik Johansson, Adel Daoud. Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities. ArXiv Preprint, 2023.
@article{jerzak2023integrating,
  title={Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities},
  author={Jerzak, Connor T. and Fredrik Johansson and Adel Daoud},
  journal={ArXiv Preprint},
  year={2023},
  volume={},
  pages={},
  publisher={}
}
[Overview][Data][Code]

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